1. Field of the Invention
The present invention relates to a detection method, particularly to an object image detection method.
2. Description of the Related Art
Object image detection technologies have been applied to many technical fields. The parameters used in an object image detection method should vary or be modified with the detected object. The most common object image detection methods include the statistic method and the neural network method recently. These two common object image detection methods are further discussed below.
Face detection technologies are used to exemplify the statistic method, wherein Principle Component Analysis (PCA) is adopted. In one application thereof, a face training sample and a non-face training sample are respectively divided into a plurality of clusters and the clusters are compared with each feature of an object image to determine the distance therebetween. In the distance evaluation, the feature vectors of all the clusters are first worked out; next, the Mahalanobis distances between the object image and the feature vectors are calculated to determine the similarities between the object image and the clusters. Next, the object image is projected to each cluster to calculate the distance between the object image and the cluster center. When most features of the object image meet the face training sample, the object image is determined to be a human face. When most features of the object image meet the non-face training sample, the object is determined to be not a human face. In another application, the face detection technology is based on the Bayesian image analysis, wherein the features of an object image are first obtained from 1-D Haar transform images perpendicular to each other, and the projected histograms. Next, the probability density functions of a face sample and a non-face sample are constructed. Next, based on the face and non-face probability density functions, the image features are converted into input vectors. Then, whether the input image is a human face is determined with the Bayesian method.
In the neural network method, every pixel of an image is usually as an input to train the neural network classifier. In such an approach, what is input is the original image. In other words, the image without feature extraction is used to train a neural network classifier. Therefore, the complexity of calculation increases, which impairs applications needing a high speed image processing. To solve such a problem, there is another approach of the neural network method, wherein local features of an image are modularized beforehand. The modularized features are input to separately train different neural network classifiers. Although the modularized features come from an identical image, no connection exists between them in training. Therefore, a special module is used to integrate the modularized features to complete the image detection task. For example, when the object is a human face, the modularized features may be: eyes, mouth, nose, etc. In such a case, the special module integrating the modularized features may be a complexion-filtering module. When a neural network classifier identifies a human face, the modularized features of eyes, mouth, and nose, together with the complexion-filtering module, are used in comparison to determine whether the input image is a human face.
Besides the abovementioned two object image detection methods, the adaboost algorithm, support vector machine (SVM), multi-information objection methods also prevail in industrial and academic fields. Among them, the adaboost algorithm is particularly popular because it has a very fast detection speed. However, the adaboost algorithm needs a very long training time because a great number of features need screening in training. Thus, the training results are unlikely to be fast obtained, and the usefulness of the adaboost algorithm is decreased.
As the statistic method filters out unnecessary data beforehand, it has shorter training time. However, the statistic method has lower detection accuracy and lower detection efficiency. For the neural network method, the training efficiency correlates with the complexity of the object image. A simpler object image needs less training time and detection time but has lower detection accuracy. A more complicated object image needs more detection time but has higher detection accuracy. The adaboost algorithm has higher detection speed but needs longer training time. Thus, the conventional statistic method, neural network method and adaboost algorithm respectively have their weaknesses and strengths. However, generally to speak, they are all unlikely to extensively apply to realtime object detection systems.
Accordingly, the present invention proposes an object image detection method, which integrates the conflicting strengths to simultaneously achieve the speed and accuracy of object detection.